Gpt Imagen

GPT Imagen

Generates or edits images for the current project (e.g., website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, infographics). Defaults to gpt-image-2 and the OpenAI Image API, and prefers the bundled CLI for deterministic, reproducible runs.

When to use

• Generate a new image (concept art, product shot, cover, website hero)
• Edit an existing image (inpainting, masked edits, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
• Batch runs (many prompts, or many variants across prompts)

Decision tree (generate vs edit vs batch)

• If the user provides an input image (or says “edit/retouch/inpaint/mask/translate/localize/change only X”) → edit
• Else if the user needs many different prompts/assets → generate-batch
• Else → generate

Workflow

1. Decide intent: generate vs edit vs batch (see decision tree above).
2. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input image(s)/mask(s). For multi-image edits, label each input by index and role; for edits, list invariants explicitly.
3. If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
4. Augment prompt into a short labeled spec (structure + constraints) without inventing new creative requirements.
5. Run the bundled CLI (scripts/image_gen.py) with sensible defaults (see references/cli.md).
6. For complex edits/generations, inspect outputs (open/view images) and validate: subject, style, composition, text accuracy, and invariants/avoid items.
7. Iterate: make a single targeted change (prompt or mask), re-run, re-check.
8. Save/return final outputs and note the final prompt + flags used.

Temp and output conventions

• Use tmp/gpt-imagen/ for intermediate files (for example JSONL batches); delete when done.
• Write final artifacts under output/gpt-imagen/ when working in this repo.
• Use --out or --out-dir to control output paths; keep filenames stable and descriptive.

Dependencies (install if missing)

Prefer uv for dependency management.

Python packages:

uv pip install openai pillow

If uv is unavailable:

python3 -m pip install openai pillow

Environment

• OPENAI_API_KEY must be set for live API calls.

If the key is missing, give the user these steps:

1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys
2. Set OPENAI_API_KEY as an environment variable in their system.
3. Offer to guide them through setting the environment variable for their OS/shell if needed.

• Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.

If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.

Defaults & rules

• Use gpt-image-2. There is no mini variant in this generation; if the user asks for a cheaper/faster model, say so rather than silently downgrading.
• Assume the user wants a new image unless they explicitly ask for an edit.
• Require OPENAI_API_KEY before any live API call.
• Use the OpenAI Python SDK (openai package) for all API calls; do not use raw HTTP.
• If the user requests edits, use client.images.edit(...) and include input images (and mask if provided).
• Prefer the bundled CLI (scripts/image_gen.py) over writing new one-off scripts.
• Do not modify scripts/image_gen.py unless the user asks for a capability change or bug fix.
• If the result isn’t clearly relevant or doesn’t satisfy constraints, iterate with small targeted prompt changes; only ask a question if a missing detail blocks success.

Prompt augmentation

Reformat user prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new requirements.

Use-case taxonomy (exact slugs)

Classify each request into one of these buckets and keep the slug consistent across prompts and references.

Generate:

• photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.
• product-mockup — product/packaging shots, catalog imagery, merch concepts.
• ui-mockup — app/web interface mockups that look shippable.
• infographic-diagram — diagrams/infographics with structured layout and text.
• logo-brand — logo/mark exploration, vector-friendly.
• illustration-story — comics, children’s book art, narrative scenes.
• stylized-concept — style-driven concept art, 3D/stylized renders.
• historical-scene — period-accurate/world-knowledge scenes.

Edit:

• text-localization — translate/replace in-image text, preserve layout.
• identity-preserve — try-on, person-in-scene; lock face/body/pose.
• precise-object-edit — remove/replace a specific element (incl. interior swaps).
• lighting-weather — time-of-day/season/atmosphere changes only.
• background-extraction — transparent background / clean cutout.
• style-transfer — apply reference style while changing subject/scene.
• compositing — multi-image insert/merge with matched lighting/perspective.
• sketch-to-render — drawing/line art to photoreal render.

Quick clarification (augmentation vs invention):

• If the user says “a hero image for a landing page”, you may add layout/composition constraints that are implied by that use (e.g., “generous negative space on the right for headline text”).
• Do not introduce new creative elements the user didn’t ask for (e.g., adding a mascot, changing the subject, inventing brand names/logos).

Template (include only relevant lines):

Use case: <taxonomy slug>
Asset type: <where the asset will be used>
Primary request: <user's main prompt>
Scene/background: <environment>
Subject: <main subject>
Style/medium: <photo/illustration/3D/etc>
Composition/framing: <wide/close/top-down; placement>
Lighting/mood: <lighting + mood>
Color palette: <palette notes>
Materials/textures: <surface details>
Quality: <low/medium/high/auto>
Input fidelity (edits): <low/high>
Text (verbatim): "<exact text>"
Constraints: <must keep/must avoid>
Avoid: <negative constraints>

Augmentation rules:

• Keep it short; add only details the user already implied or provided elsewhere.
• Always classify the request into a taxonomy slug above and tailor constraints/composition/quality to that bucket. Use the slug to find the matching example in references/sample-prompts.md.
• If the user gives a broad request (e.g., "Generate images for this website"), use judgment to propose tasteful, context-appropriate assets and map each to a taxonomy slug.
• For edits, explicitly list invariants ("change only X; keep Y unchanged").
• If any critical detail is missing and blocks success, ask a question; otherwise proceed.

Examples

Generation example (hero image)

Use case: stylized-concept
Asset type: landing page hero
Primary request: a minimal hero image of a ceramic coffee mug
Style/medium: clean product photography
Composition/framing: centered product, generous negative space on the right
Lighting/mood: soft studio lighting
Constraints: no logos, no text, no watermark

Edit example (invariants)

Use case: precise-object-edit
Asset type: product photo background replacement
Primary request: replace the background with a warm sunset gradient
Constraints: change only the background; keep the product and its edges unchanged; no text; no watermark

Prompting best practices (short list)

• Structure prompt as scene -> subject -> details -> constraints.
• Include intended use (ad, UI mock, infographic) to set the mode and polish level.
• Use camera/composition language for photorealism.
• Quote exact text and specify typography + placement.
• For tricky words, spell them letter-by-letter and require verbatim rendering.
• For multi-image inputs, reference images by index and describe how to combine them.
• For edits, repeat invariants every iteration to reduce drift.
• Iterate with single-change follow-ups.
• For latency-sensitive runs, start with quality=low; use quality=high for text-heavy or detail-critical outputs.
• For strict edits (identity/layout lock), consider input_fidelity=high.
• If results feel “tacky”, add a brief “Avoid:” line (stock-photo vibe; cheesy lens flare; oversaturated neon; harsh bloom; oversharpening; clutter) and specify restraint (“editorial”, “premium”, “subtle”).

More principles: references/prompting.md. Copy/paste specs: references/sample-prompts.md.

Guidance by asset type

Asset-type templates (website assets, game assets, wireframes, logo) are consolidated in references/sample-prompts.md.

CLI + environment notes

• CLI commands + examples: references/cli.md
• API parameter quick reference: references/image-api.md
• If network approvals / sandbox settings are getting in the way: references/codex-network.md

Output

• Generated or edited image files saved to output/gpt-imagen/ (or user-specified path)
• Final prompt and CLI flags used, logged for reproducibility

Verification

• Output image exists and is a valid PNG/JPEG
• Subject, style, and text match the prompt spec
• Invariants (for edits) are preserved; avoid items are absent

Reference map

• references/cli.md: how to run image generation/edits/batches via scripts/image_gen.py (commands, flags, recipes).
• references/image-api.md: what knobs exist at the API level (parameters, sizes, quality, background, edit-only fields).
• references/prompting.md: prompting principles (structure, constraints/invariants, iteration patterns).
• references/sample-prompts.md: copy/paste prompt recipes (generate + edit workflows; examples only).
• references/codex-network.md: environment/sandbox/network-approval troubleshooting.

Sibling skills

• gemini-imagen — same shape, different provider. Choose Gemini for multi-image composition, native 4K/extreme aspect ratios, or text rendering inside images. Choose this skill (OpenAI) when the user asks for gpt-image-2, needs precise inpainting/masking, or has only an OPENAI_API_KEY.